Manuel Salvador

A Bayesian priorization procedure for AHP-group decision making

Abstract This paper proposes a new priorization procedure for Analytic Hierarchy Process Group Decision Making (AHP-GDM). Unlike the methods normally employed in AHP-GDM, this process does not require intermediate filters for the actors’ initial judgements. The procedure is based on a Bayesian analysis of the problem and in general, it provides more efficient estimates than the techniques conventionally applied in the literature for AHP-GDM: aggregation of individual judgements (AIJ) and aggregation of individual priorities (AIP). The proposed procedure naturally extends to the analysis of incomplete and/or imprecise pairwise comparison matrices, enhancing realism, practicality and scope. The methodology has been illustrated by the analysis of a case study.

Consensus Building in AHP-Group Decision Making: A Bayesian Approach

This paper examines consensus building in AHP-group decision making from a Bayesian perspective. In accordance with the multicriteria procedural rationality paradigm, the methodology employed in this study permits the automatic identification, in a local context, of “agreement” and “disagreement” zones among the actors involved. This approach is based on the analysis of the pairwise comparison matrices provided by the actors themselves. In addition, the study integrates the attitudes of the actors implicated in the decision-making process and puts forward a number of semiautomatic initiatives for establishing consensus. This information is given to the actors as the first step in the negotiation processes. The knowledge obtained will be incorporated into the system via the learning process developed during the resolution of the problem. The proposed methodology, valid for the analysis of incomplete or imprecise pairwise comparison matrices, is illustrated by an example.

Selecting The Rank Of The Cointegration Space And The Form Of The Intercept Using An Information Criterion

In this paper we consider a family of penalized likelihood criteria for determining the rank of the cointegration space, the number of lags, and the form of the intercept in vector autoregressions with possibly integrated processes. The paper provides a general consistency result for a class of model determination procedures in which the penalty depends on a simple parameter count only.

Share prices and accounting variables: a hierarchical Bayesian analysis

Purpose – The purpose of this paper is to examine the relevance of accounting variables to explain the evolution of a companys share price; and more specifically, we analyse the influence of cash flow and book value on the evolution of the share price, taking into account certain covariates which have traditionally been regarded as helping explain this effect. Design/methodology/approach – A hierarchical Bayesian model is used to analyse the relevance of the accounting figures considered by the markets. Findings – The empirical results obtained show that the firm size and the speed of asset turnover are a companys most relevant features, which is indirectly consistent with the theory of company life cycles. Originality/value – The empirical results obtained are of value to support the validity of company life cycle theory.

Understanding the behavior of financial ratios: the adjustment process

This paper contributes to our understanding of the behavior of financial ratios by means of a hierarchical Bayesian analysis of the partial adjustment model of financial ratios presented in Davis and Peles [Acc. Rev. 68 (1993) 725]. Such an approach allows us to make a robust estimate of the average adjustment coefficient of a set of firms. The proposed methodology is applied to the analysis of a number of financial ratios considered in the above-mentioned paper corresponding to a sample of US manufacturing firms.

Automatic monitoring and intervention in multivariate dynamic linear models

An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in a matrix normal dynamic linear model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. The procedure is illustrated with two empirical examples taken from the literature.

Systemic decision making in AHP: a Bayesian approach

Systemic decision making is a new approach for dealing with complex multiactor decision making problems in which the actors’ individual preferences on a fixed set of alternatives are incorporated in a holistic view in accordance with the “principle of tolerance”. The new approach integrates all the preferences, even if they are encapsulated in different individual theoretical models or approaches; the only requirement is that they must be expressed as some kind of probability distribution. In this paper, assuming the analytic hierarchy process (AHP) is the multicriteria technique employed to rank alternatives, the authors present a new methodology based on a Bayesian analysis for dealing with AHP systemic decision making in a local context (a single criterion). The approach integrates the individual visions of reality into a collective one by means of a tolerance distribution, which is defined as the weighted geometric mean of the individual preferences expressed as probability distributions. A mathematical justification of this distribution, a study of its statistical properties and a Monte Carlo method for drawing samples are also provided. The paper further presents a number of decisional tools for the evaluation of the acceptance of the tolerance distribution, the construction of tolerance paths that increase representativeness and the extraction of the relevant knowledge of the subjacent multiactor decisional process from a cognitive perspective. Finally, the proposed methodology is applied to the AHP-multiplicative model with lognormal errors and a case study related to a real-life experience in local participatory budgets for the Zaragoza City Council (Spain).

Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets

As a result of increased mobility patterns of workers, explaining labor flows and partitioning regions into local labor markets (LLMs) have become important economic issues. For the former, it is useful to understand jointly where individuals live and where they work. For the latter, such markets attempt to delineate regions with a high proportion of workers both living and working. To address these questions, we separate the problem into two stages. First, we introduce a stochastic modeling approach using a hierarchical spatial interaction specification at the individual level, incorporating individual-level covariates, origin (O) and destination (D) covariates, and spatial structure. We fit the model within a Bayesian framework. Such modeling enables posterior inference regarding the importance of these components as well as the O-D matrix of flows. Nested model comparison is available as well. For computational convenience, we start with a minimum market configuration (MMC) upon which our model is overlaid. At the second stage, after model fitting and inference, we turn to LLM creation. We introduce a utility with regard to the performance of an LLM partition and, with posterior samples, we can obtain the posterior distribution of the utility for any given LLM specification which we view as a partition of the MMC. We further provide an explicit algorithm to obtain good partitions according to this utility, employing these posterior distributions. However, the space of potential market partitions is huge and we discuss challenges regarding selection of the number of markets and comparison of partitions using this utility. Our approach is illustrated using a rich dataset for the region of Aragon in Spain. In particular, we analyze the full dataset and also a sample. Future data collection will arise as samples of the working population so assessing population level inference from the sample is useful.

Automatic selective intervention in dynamic linear models

In this paper we propose an algorithm to carry out the monitoring and retrospective intervention process in Dynamic Linear Models, both selectively and automatically. The algorithm is illustrated by analysing several series taken from the literature, in which the proposed procedure is shown to perform better than the scheme proposed by West & Harrison (1997, Chapter 11).

Analysis of housing price by means of STAR models with neighbourhood effects: a Bayesian approach

In this paper, we extend the Bayesian methodology introduced by Beamonte et al. (Stat Modelling 8:285–311, 2008) for the estimation and comparison of spatio-temporal autoregressive models (STAR) with neighbourhood effects, providing a more general treatment that uses larger and denser nets for the number of spatial and temporal influential neighbours and continuous distributions for their smoothing weights. This new treatment also reduces the computational time and the RAM necessities of the estimation algorithm in Beamonte et al. (Stat Modelling 8:285–311, 2008). The procedure is illustrated by an application to the Zaragoza (Spain) real estate market, improving the goodness of fit and the outsampling behaviour of the model thanks to a more flexible estimation of the neighbourhood parameters.